Advances in computer technology (e.g., microprocessor speed, memory capacity, data transfer bandwidth, software functionality, and the like) have generally contributed to increased computer application in various industries. For example, computer based decision-support systems are commonly employed in recognition systems, such as Optical Character Recognition (OCR), and related text recognition applications.
Typically, scanners or optical imagers were initially developed to “digitize” pictures (e.g., input images into a computing system). Subsequently, such systems were applied to other printed and typeset material, and OCR systems gradually extended to a plurality of computer applications. In general, OCR technology is tuned to recognize limited or finite choices of possible types of fonts. Such systems can in general “recognize” a character by comparing it to a database of pre-existing fonts. If a font is deemed incoherent, the OCR technology returns unidentifiable or non-existing characters, to indicate non-recognition of such incoherent text.
Moreover, handwriting recognition has proved to be an even more challenging scenario than text recognition. In general, a person's handwriting exemplifies an individualistic style that shows through penmanship. Accordingly, by its very nature, handwriting patterns exhibit diverse forms, even for the same character. Obviously, storing every conceivable form of handwriting for a particular character is not feasible.
Various approaches have been developed to recognize patterns associated with such handwritten characters. Most handwriting recognition systems employ recognizers based on Neural Nets, Hidden Markov Models (HMM) or a K-Nearest-Neighbor (KNN) approach. In general, such systems perform reasonably well at the task of classifying characters based on their total appearance. For example, a level of similarity can be determined by generating a distance measure between patterns.
However, the recognition of handwritten text in images, commonly known as offline handwriting recognition, remains a challenging task. Significant work is still to be done before large scale commercially viable systems can be efficiently built. These problems are further magnified by non-Latin languages/scripts such as Arabic, Farsi, and the like—wherein less research effort has been allocated for addressing the associated recognition problems involved.
Typically, majority of research in Arabic offline recognition has been directed to numeral and single character recognition. Few examples exist where the offline recognition of Arabic words problem is addressed. Recent construction of standard publicly available databases of handwritten Arabic text images (e.g., IFN/INIT database) has slowly encouraged further research activities for these scripts/languages.
In contrast, for Latin scripts, Hidden Markov Model (HMM) based approaches have dominated the space of offline cursive word recognition. In a typical setup, a lexicon is provided to constrain the output of the recognizer. An HMM can then be built for every word in the lexicon and the corresponding likelihood (probability of data being generated by the model) is computed. In general, the most likely interpretation is then postulated to be the correct one.
In the few reported approaches to Arabic text recognition, similar approaches like Latin text recognition methodologies have been typically employed. Moreover, various attempts performed to modify the preprocessing and feature extraction phases to accommodate the different nature of the Arabic writing script have not proved to be efficient. In addition, such attempts in general do not exploit the unique properties of Arabic script such as condition joining rules for recognition purposes.